VCG vector loop bifurcation

ABSTRACT

A system for determining T wave bifurcation that includes three or more ECG leads configured to receive an ECG signal and a first computing device including a processor coupled to a memory, the processor and the memory configured to perform operations including: generating at least two orthogonal ECG vectors based on the ECG signal of a patient, processing the at least two orthogonal ECG vectors to determine a loop trajectory of at least a portion of the ECG signal, identifying a trajectory bifurcation by comparing the loop trajectory to a control loop trajectory for a plurality of cardiac cycles, and determining an indicator of a cardiac event based on the trajectory bifurcation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of and claims priority toU.S. patent application Ser. No. 14/628,525, filed on Feb. 23, 2015, nowU.S. Pat. No. 9,545,209, which claims priority under 35 USC § 119(e) toU.S. Patent Application Ser. No. 61/945,424, filed on Feb. 27, 2014. Theentire content of each application is hereby incorporated by reference.

TECHNICAL FIELD

This document relates to identification and management of patients atrisk of cardiac events, and in particular to systems and techniques fordetermining T wave bifurcation.

BACKGROUND

The most common tool used for cardiac diagnosis is based onelectrocardiogram (ECG) measurement and interpretation. Traditional ECGincludes information about the timing of cardiac electrical events andthe time intervals between two or more such events. For example,transmural repolarization, reflected by the T wave in an ECG, canindicate a plurality of abnormalities that mark susceptibility tolife-threatening arrhythmias. Such abnormalities can be associated withgenetic defects, various acquired cardiac dysfunctions, electrolytedisorders, and certain prescription and non-prescription drugs.

SUMMARY

In a general aspect, a system includes three or more ECG leadsconfigured to receive an ECG signal from a patient and a first computingdevice including a processor coupled to a memory. The processor and thememory are configured to perform operations including: generating atleast two orthogonal ECG vectors based on the ECG signal of a patient,processing the at least two orthogonal ECG vectors to determine a looptrajectory of at least a portion of the ECG signal, identifying atrajectory bifurcation by comparing the loop trajectory to a controlloop trajectory for a plurality of cardiac cycles, and determining anindicator of a cardiac event based on the trajectory bifurcation.

Embodiments can include one or more of the following features.

The loop trajectory is a test loop trajectory that includes a firstplurality of loop trajectories obtained during a first time period andthe control loop trajectory includes a second plurality of looptrajectories obtained during a second time period that is prior to thefirst time period.

The first time period includes a time period in present or recent pastand the second time period includes a time period in a more distantpast. The first time period includes a time period within 60 minutes ofa present time and the second time period includes a time period within72 hours of the present time. The first time period and the second timeperiod are separated by at least 5 minutes.

The portion of the ECG signal includes a QRS wave. The portion of theECG signal includes at least one of a T wave, a P wave and an S wave.

The identification of the trajectory bifurcation further includesmeasuring a degree of trajectory bifurcation between the loop trajectoryand the control loop trajectory. The measurement of the degree of thetrajectory bifurcation is based on a statistical analysis. Thestatistical analysis is a non-Gaussian statistical analysis.

The cardiac event includes an impending acute degeneration of apatient's medical condition. The impending acute degeneration is one ofa cardiac arrest and a traumatic arrest.

The identification of the trajectory bifurcation further includesidentifying landmarks in the loop trajectory and in the control looptrajectory. The identification of the trajectory bifurcation furtherincludes determining a measure of a shape uncertainty of the looptrajectory and the control loop trajectory. Determining the measure ofthe shape uncertainty is based on a statistical analysis of anuncertainty boundary of the loop trajectory and the control looptrajectory.

The first computing device includes a defibrillator. The first computingdevice includes a mobile computing device. The first computing device isconfigured to transmit the indicator of the cardiac event to adefibrillator.

In a general aspect, an apparatus includes a computer readable mediumstoring instructions for causing a computing system to performoperations including: receiving, from two or more ECG leads, an ECGsignal of a patient, generating at least two orthogonal ECG vectorsbased on the ECG signal, processing the at least two generallyorthogonal ECG vectors to determine a loop trajectory of at least aportion of the ECG signal, identifying a trajectory bifurcation bycomparing the loop trajectory to a control loop trajectory for aplurality of cardiac cycles and determining an indicator of a cardiacevent based on the trajectory bifurcation.

Embodiments may include one or more of the following features.

The cardiac event includes an impending acute degeneration of apatient's medical condition. The impending acute degeneration is one ofa cardiac arrest and a traumatic arrest. The apparatus is adefibrillator. The defibrillator performs operations includingdelivering a treatment to the patient based on the indicator of acardiac event.

In a general aspect, a computer-implemented method executed by one ormore processors includes: receiving, from two or more ECG leads, an ECGsignal, generating at least two orthogonal ECG vectors based on the ECGsignal, processing the at least two generally orthogonal ECG vectors todetermine a loop trajectory of at least a portion of the ECG signal,identifying a trajectory bifurcation by comparing the loop trajectory toa control loop trajectory for a plurality of cardiac cycles, anddetermining an indicator of a cardiac event based on the trajectorybifurcation.

Embodiments may include one or more of the following features.

Processing the at least two orthogonal ECG vectors to determine a looptrajectory includes: detecting an onset of a portion of the ECG signaland an end of the portion of the ECG signal, isolating the portion ofthe ECG signal based on detecting the onset of the portion of the ECGsignal and an end of the portion of the ECG signal, and generating avector loop based of the portion of the ECG signal.

The identification of the trajectory bifurcation includes calculating anarea of the loop trajectory and subtracting the area of the looptrajectory from an area of the control loop trajectory. Theidentification of the trajectory bifurcation further includes: comparingthe trajectory bifurcation of at least three consecutive cardiac cyclesof the plurality of cardiac cycles, determining a trend of thetrajectory bifurcation and based on the trend, defining an episodictrajectory bifurcation.

The techniques described herein can have one or more of the followingadvantages. The systems and methods described herein provide a softwareanalytic tool that provides an accurate prediction of impending acutedegeneration of a patient's medical condition. The software analytictool can automatically analyze VCG loops of patients to non-invasivelydetect and predict pending cardiac conditions that can constantly changeover time. In some examples, a trajectory bifurcation can be identifiedbased on comparison of a loop trajectory to previously stored controlloop trajectories (e.g., a control loop generated based on an averagingof several prior loop trajectories). For example, a system can generatethree orthogonal ECG vectors based on ECG data. By processing theorthogonal vectors, a loop trajectory can be determined and compared toa stored control loop trajectory. Differences in the loop trajectoriescan be used to identify trajectory bifurcation. A trend of differencesobserved over time allows a caregiver to view information about thepatient's cardiac rhythm at any point in time, which can aid indiagnosing and treating the patient. For instance, re-displaying aportion of the patient's ECG trace from a point in time when treatmentwas started and/or a portion of the patient's ECG trace prior toreceiving a treatment can provide information that can be used todiagnose the patient's condition. Knowledge of the patient's likelydiagnosis can inform treatment of the patient, both at a rescue sceneand in a hospital setting. The ability to view the historicalinformation about treatment and health status of the patient at therescue scene or at another location, such as at a hospital, can enableskilled caregivers to make informed treatment decisions even if thosecaregivers were not present when the information was recorded.

It is appreciated that methods in accordance with the present disclosurecan include any combination of the aspects and features describedherein. That is to say that methods in accordance with the presentdisclosure are not limited to the combinations of aspects and featuresspecifically described herein, but also include any combination of theaspects and features provided.

The details of one or more embodiments are set forth in the accompanyingdrawings and the description below. Other features and advantages willbe apparent from the description, drawings, and claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a graphical example of T-wave alternans and prolonged QTinterval.

FIG. 2 is a graphical example of a mean vector of the QRS complex asprojected on to the frontal plane.

FIG. 3 is a graphical example of a monocardiogram.

FIG. 4 is a graphical example of a vector of the VCG and its motionpresented as projections of the vectorcardiograph onto one or moreplanes.

FIG. 5 is a graphical example of a VCG vector loop.

FIG. 6 is a schematic illustration of an example patient monitoringsystem.

FIG. 7 is an example of a graphical user interface displaying patientdata, including a trajectory bifurcation.

FIG. 8 is an example of a graphical user interface including a riskwaveform.

FIG. 9 is an example of a graphical user interface including a controlloop and a test loop.

FIG. 10 is a flow chart of a process for identifying trajectorybifurcation and calculating a risk score.

FIG. 11 is a flow chart of an example process for identifying atrajectory bifurcation.

FIG. 12 is a schematic illustration of an example system.

DETAILED DESCRIPTION

Implementations of the present disclosure are generally directed tosystems and methods for measuring a degree of trajectory bifurcation anddetermining an impending acute degeneration of a patient's medicalcondition based on the degree of trajectory bifurcation. The measurementof the trajectory bifurcation can include identifying, viewing andanalyzing T wave alternans (TWA).

T wave alternans (TWA) represent periodic beat-to-beat variations in theamplitude or shape of the T wave in an electrocardiogram (ECG or EKG).FIG. 1 shows a graphical example of TWA and prolonged QT interval 100.TWA can include large variations (“macroscopic” TWA) that are associatedwith increased susceptibility to lethal ventricular tachycardias. TWAcan also include microvolt T wave alternans (MTWA) that are variants ofTWA and are associated with an increased risk of sudden cardiac death.MTWA can be used in patients who have had myocardial infarctions (heartattacks) or other heart damage to see if they are at high risk ofdeveloping a potentially lethal cardiac arrhythmia. The patientsidentified to be at high risk can potentially benefit from the placementof a defibrillator device, which can stop an arrhythmia and save thepatient's life.

The TWA test uses an electrocardiogram (ECG) measurement of the heart'selectrical conduction. The test looks for the presence of repolarizationalternans (e.g., TWA), which is a variation in the vector and amplitudeof the T wave component of the ECG. The amount of variation is small, onthe order of microvolts. A small variation in the vector and amplitudeof the T wave component can be detected by digital signal processingtechniques.

MTWA detects T wave alternans signals as small as one-millionth of avolt. Microvolt T wave alternans is an alternation in the morphology ofthe T wave in every other beat or AB-AB pattern. MTWA is associated withventricular arrhythmias and sudden death. In some cases, visuallydiscernible alternans have been linked to the rapid onset of ventriculartachyarrhythmias. In some examples, visually indiscernible alternans canalso be significant.

The presence of alternans can be determined using various mathematicaltools and concepts including vector and loop analysis. For example,electric forces from the heart as recorded from the body surface can berepresented as a vector force. The equilateral triangle of Einthoven canbe used to obtain the mean electric axis of the QRS complex of therecorded electrocardiogram from standard limb leads I, II, and III. FIG.2 illustrates an example mean vector 200 as projected on to the frontalplane. The angle of the vector indicates the direction of the meanvector 200, and its length indicates the mean magnitude. The quantitiesof the mean vector, including the direction and the magnitude, asprojected on the frontal plane also indicate a “sense.” The “sense”indicated by the vector can be described by the fact that the vector isdirected away from the area of greatest relative negativity of theelectric force derived from the heart toward the area of greatestrelative positivity.

As another example, electric forces from the heart as recorded from thebody surface can be represented as a “loop.” The loop can be defined asa continuous uninterrupted series of vectors that are displayed asvector quantities of the electric forces of cardiac depolarization andrepolarization recorded by the electrocardiogram. An example of a seriesof vectors 300 used for vector analysis, as graphed by Hubert Mann, isshown in FIG. 3.

Various lead configurations can be used to measure and analyzeelectrical events. In some situations, three leads can be designed torecord components of a resultant cardiac electromotive force in threemutually perpendicular directions, then the problem of deriving theresultant cardiac electromotive force is solved. For example, supposethat the potential measured by any electrocardiographic lead isrepresented by V and that the resultant cardiac electromotive force isdenoted by H or, as it is sometimes known, the “heart vector.” Frommathematical considerations, it can be shown that V=H·L, where L is thevector representing the strength of the lead being used to measure thepotential. The mathematical formula of the cardiac potential can beexpanded to the following: V=H_(x)L_(x)+H_(y)L_(y)+H_(z)L_(z), whereH_(x), H_(y), H_(z) are the three components of the heart vector andL_(x), L_(y), L_(z) are the three components of the lead vector. Themathematical formula of the cardiac potential indicates that thecomponent of the heart vector in the X direction can be measured and alead can be designed to have components (L_(x), 0, 0). For anX-direction measurement, the potential can be expressed asV_(x)=H_(x)L_(x), and if the strength Lx of the lead is known, then whenthe potential V_(x) can be measured and H_(x) can be calculated.

Over the years, a number of different electrode configurations have beendeveloped that provide a method of generating at least two orthogonalleads, though mostly the full X, Y, and Z orthogonal leads of VCG areused. Some examples of electrode configurations are the Grishman,Milnor, Wilson-Burch, Frank, Dower and the standard 12 leadconfiguration well-known in clinical practice from which the orthogonallead set can be derived. The “orthogonal” leads can be approximations oftruly orthogonal leads. The term orthogonal leads can be a practical,broader approximation of orthogonal leads, including truly orthogonalleads as a subset.

Vectorcardiography has been supplanted by the 12 lead ECG because theloops generated by the VCG are too complex for even highly trainedcardiologists to fully comprehend. In vectorcardiography (VCG),myocardial electrical activity is treated mathematically as a dipolethat is the aggregate of the electrical activity of all the cells of themyocardium. The dipole size and spatial angle are presented as a vectorwhose angle and magnitude change during a cardiac cycle. For example, inVCG, the measurement points are positioned in such a way that threederived signals correspond to three orthogonal axes (X, Y, Z).

The VCG can be conceptualized as the trajectory of the “tip” of therepresentative vector in the two or three-dimensional measurement space.In some embodiments, the VCG represents the ECG as a the motion of athree dimensional vector, but it can be easily understood by thoseskilled in the art that the representation can be formed in abidimensional space or a space with more dimensions. In the case ofhigher dimensionality, the dimensions greater than three can be composedof magnetocardiographic measurements, for example, or even of completelydifferent physiologic measurements such as pulse oximetry, near-infraredspectroscopy, end-tidal CO2, EEG or other physiologic waveforms. FIG. 4illustrates an example vector of the VCG 400 and its motion can bepresented as projections of the vectorcardiograph onto one or moreplanes (e.g., the frontal 402, sagittal 404, and horizontal 406 planes).

As described in more detail below, a trajectory bifurcation can beidentified based on comparison of a loop trajectory to previously storedcontrol loop trajectories (e.g., a control loop generated based on anaveraging of several prior loop trajectories). For example, a system cangenerate three orthogonal ECG vectors based on ECG data. By processingthe orthogonal vectors, a loop trajectory can be determined and comparedto a stored control loop trajectory. Differences in the looptrajectories can be used to identify trajectory bifurcation.

FIG. 5 shows an example path of the vector 500 in either threedimensional space, or as a projection onto a plane. At the beginning ofeach cardiac electrical cycle, just prior to the P-wave and QRS-wave,there is a period where the ECG is isoelectric. The isoelectric ECGmarks the beginning of the VCG vector loop that begins and ends at thesame isoelectric point and thus forms a loop. As can be seen in FIG. 5,the VCG can include multiple discrete loops corresponding to the QRSportion (e.g., loop 502) as well as the P-wave and T-wave portions(e.g., loop 504). The VCG portions can be treated as separate loops forthe loop analysis and identification of a trajectory bifurcation. Thetrajectory bifurcation is defined as shift of the loop from one type ofshape to another type of shape. The trajectory bifurcation can be amarker of elevated risk of impending acute degeneration of a patient'smedical condition.

In one embodiment, standard 12 lead electrode placement and dataacquisition and analysis hardware and software is used to generate a VCGvector loop. Exemplary 12 lead electrodes can be implemented in standardmedical devices, such as X-Series monitors and defibrillators producedby ZOLL Medical®, Chelmsford Mass. The device can also take the form ofa wearable defibrillator or monitor, such as the LifeVest produced byZOLL Medical®, Chelmsford, Mass., which has two approximately orthogonalleads and thus can generate a two-dimensional vector loop. Techniquessuch as QRS detection, can be used to determine the start time fiducialsfor each ECG cycle. QRS rhythm morphologies can be classified to allowfor removing intervals containing such beat classes as prematureventricular contractions (PVCs) from the analysis. It can be noted thatthe data and waveforms generated by single-lead, three-lead, five-leadand 12 lead ECGs, while they are vectors in the strict sense that theyare a concatenated list of scalar values, cannot be considered ascontaining loops nor are they under the purview of vectorcardiography.

VCG loops can be generated for the overall segment as well as the P, Tand QRS portions. In some embodiments, one or more groups of looptrajectories are collected for a duration of 10 seconds up to 30 minutes(e.g., 10 seconds up to 1 minute, 10 seconds up to 10 minutes, 10seconds up to 20 minutes, 10 seconds up to 30 minutes). The groups ofloop trajectories are of the segments (e.g., P, T, or QRS) or theoverall cycle. Multiple groups of loops can be generated at differenttimes. Each group is composed of one or more loops. The first group actsas a control group for comparison to a second group whose data wascollected more recently than the control group (e.g., the second groupof data is collected at a time subsequent to the collection of the firstgroup of data). In order to make the comparison of the loops in thedifferent groups, the shape of the loops in the control group arecharacterized. The characterization can be accomplished using aplurality of methods. For example, the characterization of the loopshape can be accomplished using statistical shape analysis.

In general, statistical shape analysis includes methods for studying thegeometrical properties of random objects invariant under translation,scaling and rotation. The loop analysis described herein can be based onimage analysis such as the statistical analysis using an Active ShapeModel that includes global constraints with respect to shape. The globalconstraints can be determined from historical data (e.g., by machinelearning) giving the model flexibility, robustness and specificity asthe model synthesizes plausible instances with respect to theobservations. In order to determine whether an object has changed shape,the shape of the object is first determined. In general, the shaperefers to all geometrical information that remains when location, scaleand rotational effects are filtered out from an object. Accordingly, theshape is invariant to Euclidean similarity transformations.

In addition to using the shape of an object in image analysis, otherparameters used in the analysis can include a landmark, an anatomicallandmark, mathematical landmarks, pseudo-landmarks, a configuration, aconfiguration matrix, and/or a configuration space, each of which isdescribed briefly below. In general, a landmark refers to a point ofcorrespondence on each object that matches between and withinpopulations. Each landmark is associated with Cartesian coordinates,that is, either with an ordered pair of coordinates in the plane or witha triple of coordinates in 3D-space.

An anatomical landmark is a point assigned by an expert that correspondsbetween objects of study in a way meaningful in the context of thedisciplinary context. In addition to the Cartesian coordinates, eachlandmark has a name denoting correspondence from shape object to shapeobject, for example, the point of the right elbow. Mathematicallandmarks are points located on an object according to some mathematicalor geometrical property of the figure. Pseudo-landmarks are constructedpoints on an object, either around the outline or in between anatomicalor mathematical landmarks. The configuration is the set of landmarks ona particular object. The configuration matrix X is the [k·m] matrix ofCartesian coordinates of k landmarks in m dimensions. The configurationspace is the space of all possible landmark coordinates. In applicationswe have k≥3 landmarks in m=2 or m=3 dimensions.

Landmarks can also be referred to as homologous points, such as nodes,vertices, anchor points, fiducial markers, model points, markers or keypoints. Various methods for automatic landmark determination have beenemployed on the loops. For example, 10 points equidistant in time areselected and the amplitude at those points, or alternatively the maximumvector amplitude and the time at which that occurs is determined. Basedin the identification of the maximum vector amplitude 5 pointsequidistant between the maximum point and the origin on each side of themaximum vector amplitude point are selected. In some implementations,the spacing can be different for each side of the maximum point due tothe fact that the maximum vector amplitude point is not equidistant fromthe origin on each side.

In order to analyze the loop trajectories determined from the measuredVCG, an alignment procedure can be used. An exemplary alignmentprocedure can include a Procrustes analysis. The alignment procedure canbe performed in the shape space. The shape space is the set of allpossible shapes of the selected object. More particularly, the shapespace Σ^(n) _(k) is the orbit shape of the non-coincident n point setconfigurations in the IR^(k) under the action of the Euclideansimilarity transformations. It can be important to understand thedimension spanned by this shape space. Given n random point vectors in kEuclidean dimensions the dimensionality is kn. The alignment procedurecan reduce the number of dimensions (e.g., the data spans a subspace ofkn). For example, the translation removes k dimensions, the uniformscaling removes one dimension and the rotation removes ½*k(k−1)dimensions. The shape space dimensionality can be defined as:M=kn−k−1−k(k−1)/2.

If a relationship between the distance in shape space and Euclideandistance in the original plane can be established, the set of shapesactually forms a Riemannian manifold containing the object class inquestion (e.g. hands), denoted as the Kendall shape space. Therelationship between the distance in shape space and Euclidean distancein the original plane is called a shape metric. The shape metrics caninclude the Hausdorff distance, the strain energy, and the Procrustesdistance. The Hausdorff distance and the strain energy compare shapeswith unequal amount of points, the Procrustes distance requirescorresponding point sets. Various shape metrics can be used in themethods described herein. Using the above-mentioned techniques ofstatistical shape analysis, the characteristics of the control group canbe statistically parameterized.

In some embodiments, at subsequent time points (e.g., at 30 second orone minute intervals) a second, “test” group of trajectories can becollected, again, for durations of 10 seconds up to 30 minutes. Theshape of the loops in the test group are characterized. Thecharacteristics of the test group are then compared to the controlgroup. The comparison techniques can include correlation matrices,Bookstein coordinates, centroid size, Procrustes analysis includingplanar, general or ordinary Procrustes analysis, the full Procrustesdistance, Hotelling's T2 or Goodall's F-test to determine differences inthe mean shape of loops for control and test groups and principalcomponent analysis.

In some examples outline or contour analysis can be used additionally oralternatively to shape analysis to analyze the control and test groupsof loops. In general, outline or contour analysis is based on digitizinga large number of points around the boundary of an object. For thosesituations in which landmarks are difficult to identify or obtain, ifthe outline can be represented by a closed curve or boundary, thenoutline analysis can substitute the shape analysis. Many shape objectsthat do not have clearly identifiable landmarks nevertheless can beanalyzed successfully under such conditions. Use of outline analysis canbe particularly useful in situations where paucity of landmarks isimmediately apparent, although the representations of the loops can beeasily differentiated visually.

In yet another example, Bayesian statistical shape analysis can also beemployed when comparing the test to the control group. In some examples,a variation in landmark position can be used to identify differencesbetween the control loops and test loops. One method for analysis ofvariation in landmark position is generally regarded as “Procrustes.”This method includes a least-squares alignment of a set of landmarkfeatures to a mean shape, and this can be followed by eigenvectoranalysis of the linear correlations in variation around that mean.Methods as described in this paper or others on the subject can be usedto compare the shape characterization for the first group, i.e. thecontrol or baseline group of loops with the second, “test,” loop orgroup of loops. Such measurements as principal component analysis,Procrustes distance, Hausdorff distance, or centroid size can beemployed to compare the two groups.

Analysis of the baseline (e.g., control) group of loops with the second,“test,” loop or group of loops can be used to determine a risk ofimpending acute degeneration of a patient's medical condition. The riskof impending acute degeneration of a patient's medical condition intocardiac arrest or other severe cardio-pulmonary conditions can becalculated by a variety of methods. In some examples, the risk can becalculated using a scoring model based on a mathematical model such asone based on logistic regression. Exemplary logistic regression modelsthat can be used to calculate the risk include univariate analysis ormultivariate non-linear regression.

In one embodiment, the logistic regression mathematical model can beused, for example, on data from samples of cardiovascular (CVS) andnon-cardiovascular (non-CVS) patients. The logistic regressionmathematical model can be fitted separately with a combination ofdemographic parameters (age), vital signs and other ECG parameters forthe CVS and non-CVS patients. The prediction performance can beinvestigated through Receiver Operating Characteristic (ROC) analysis aswell as Sensitivity, Specificity, Positive Predictive Value (PPV) andNegative Predictive Value (NPV). Based on the logistic model, a riskscore that can be a score from 0-100% is generated at regular intervals,typically on the order of every 10 seconds. In other examples, the riskscore can be generated at regular intervals such as 30 seconds, 1minute, or 5 minutes. The logistic model can take into account thefirst, second and higher order derivatives of the shape distance betweenthe first and second groups of loops. In other words, if the distance isdiverging more rapidly, that is a sign of the patient's conditiondegenerating more rapidly and this in itself will elevate the riskscore. In some examples, the intervals at which the risk score iscalculated is a function of the perceived risk. For example, thefrequency of the calculation can be increased upon identification of anincreased risk level (e.g., upon a determination that a risk levelexceeds a threshold risk level).

Separate risk scores can be calculated for different time periods, forinstance, separate risk score for risk of an event before 10 minutes, 1hour, 2 hours, 4 hours, 8 hours, 12 hours, 24 hours, 48 hours, and 72hours. The patient monitoring device can display the risk scores as alist, can display one or more from the list based on user inputselection from the user, or can show the risk displayed as a curve onthe display. As such, a time-dependent risk calculation and evolutioncan be generated and displayed to the patient or medical professional.

FIG. 6 illustrates an example patient monitoring configuration 600. Theexample, patient monitoring configuration 600 includes three or moreelectrodes 602, attached to various locations on the body surface of thepatient 604. In this example, a 62 lead ECG with 62 electrodes is shown.The electrodes 602 are electrically coupled to the patient monitoringdevice 606. An example of a patient monitoring device 606 can be astandard ECG monitoring device, a portable ECG monitoring device, adefibrillator, a smartphone, a personal digital assistant (PDA), alaptop, a tablet personal computer (PC), a desktop PC, a set-top box, aninteractive television and/or combinations thereof or any other type ofmedical device capable to record and process ECG signals.

In the pictured example, the patient monitoring device 606 is configuredto acquire and display a VCG signal 608 via the electrode package 602.This VCG signal can be used in the identification of trajectorybifurcation based on an analysis of a loop or group of loop trajectories(e.g., represented as solid lines) compared to a control group of loops(e.g., represented as dashed lines) as described herein. The monitoringdevice 606 also enables user input via the user interface 610 andadditional control buttons 612 and 614. In some implementations, thecontrol buttons 612 can enable a user to select one of a plurality ofavailable graphical user interfaces 610, described in detail withreference to FIGS. 7-9. Graphical user interfaces 610 provide a medicalprofessional with various representations of the VCG signal and otherinformation that can aid in developing a treatment plan for the patient604. In some implementations, the control buttons 614 can enable a userto initiate, stop or modify particular actions that can be performed bythe patient monitoring device 606. Actions that can be initiated,stopped or modified by using the buttons 614 can include the selectionof processing method, selection of an alarm threshold, suspension ofalarm, recording of data and transmitting data over the network to aremote device.

FIG. 7 illustrates an example graphical user interface (GUI) 700 tographically represent patient data on the display of a device associatedwith the patient monitoring device 606 (FIG. 6), such as a computer or aremote display device. The example patient data discussed hereincorresponds to real-time or recorded VCG data. In particular, GUI 700provides cardiac information relating to data collected fromelectrocardiogram monitors coupled to the patient.

In some implementations, GUI 700 enables a user to select a patient(e.g., a medical professional might be able to access information aboutmultiple patients under their care). For each selected patient, GUI 700can display patient statistics, recorded data, determined data and othermedical information related to the patient. The determined data that canbe displayed by GUI 700 includes the VCG 712 and parameters derived fromVCG, such as the trajectory bifurcation and a risk indicator 713 (asdescribed in more detail below).

The example illustrated in FIG. 7 displays both patient statistics andrecorded data. A particular display of patient information can beselected by a user interacting with the header 702, which includesmultiple tabs. In some implementations, the header 702 includes anoverview tab 702 a, a measurements tab 702 b, a risk assessment tab 702c and a VCG comparison tab 702 d. In some implementations, a user of thepatient monitoring device can personalize the header 702, by adding ordeleting a number of tabs. For example, the user can add a tab forpatient history.

GUI 700 corresponds to an overview tab. The GUI 700 includes a patientstatistics area 704 and a patient data area 706. Patient statistics area704 includes information such as a name, gender, weight, height, and ageof the particular patient, to which the displayed patient datacorresponds. The patient statistics area 704 can also include thecurrent date and time, and other information (e.g., heart rate, PRinterval, QT interval, QRS duration). The patient statistics area 704can also include an indicator 713 associated with the calculated riskscore. For example, the risk score can be displayed as a numeric valueand can also be color-coded, for instance it can turn from green to redif the risk score exceeds a threshold value of, for example, 70%.

Patient data area 706 includes a first display portion 708, and a seconddisplay portion 710. The first display portion 708 and the seconddisplay portion 710 each provide patient data as full traces of patientdata for a particular period of time, graphically representing the datacollected over the particular period of time. As illustrated in FIG. 7,the first display portion 708 can display a VCG 712 corresponding tomultiple cardiac cycles. In some implementations, the real time motionof cardiac vectors in 3D space can be indicated by a marker 711. Themarker 711 can be a geometrical shape (e.g., shown here as a triangle).In other examples, the VCG 712 corresponds to multiple cardiac cyclesand the display can highlight the display of the current cycle. Forexample the highlighting of a physiological waveform can involve usingthicker lines of darker colors.

The second display portion 710 can include additional patient data. Forexample, the signal displayed in the second display portion 710 can beblood oxygen saturation (SpO2), as displayed in FIG. 7, blood pressure,or any other signal that can be recorded and displayed by the patientmonitoring device. The risk score can also be represented as a waveformalong with SpO2 or other physiologic waveforms. In some implementations,a user can modify the data displayed within patient data area 706. Thedata can be modified by a user clicking on the title of a displayportion 708 or 710 enabling a selection from an available drop downlist.

Referring to FIG. 8, an example GUI 800, corresponding to a selectedrisk assessment tab 702 c, is illustrated. The GUI 800 includes apatient statistics area 802 and a patient data area 804. The patientstatistics area 802 can include information similar to the patientstatistics area 704 shown in FIG. 7. The patient data area 804 caninclude various information to enable a caregiver to assess risk of animpending acute event. The patient data area 804 includes variousportions 808, 809, 810, 812, displaying different information.

The first display portion 808 can illustrate the calculated risk scorewaveform 820. In general the risk score waveform 820 shows a percentagerisk (e.g., scaled from 0 to 100% risk) as a function of time. Curve 820provides a historical view of the calculated risk over a period of time.This enables a caregiver to view whether the patient's risk level isincreasing or decreasing and modify treatment accordingly.

The second display portion 809 can illustrate the predicted risk for apatient. For example, a risk score can be calculated for various periodsof time in the future (e.g., risk of an event within 10 minutes, 80minutes, 1 hour, 8 hours, 10 hours, 24 hours, 48 hours, 72 hours). Thisinformation can be displayed graphically to allow a caregiver to assessboth the current risk and future risk of an event.

In general, as shown in FIG. 7, the VCGs are displayed as loops. Otherdisplayed components can include, but are not limited to, loop centroidarea, loop angle, loop width. The third display portion 810 can displaythe speed of the VCG within the T wave loop. The speed can be definedas: vx(t)=Δv/Δt, where Δv=∥v(t)−v(t+Δt)∥ and vx(t) represents theprojection of the speed on the x axis, as function of time. The fourthdisplay portion 812 can display one of the phase angles of the VCG,which can be determined can be based on the following equation: cosφ(t)=v_(x)(t)/√{square root over (v_(x) ²+v_(y) ²+v_(z) ²)}. In someadditional examples, conventional ECG waveform can be shown on thedisplay with a box containing the VCG loop that overlays or is justslightly above or below the ECG trace. The box can then be slid alongthe ECG trace with the displayed loop in the box corresponding to theECG cycle underneath the box on the display.

Referring to FIG. 9, an example GUI 900, corresponding to a selected VCGcomparison tab 702 d, is illustrated. The display 900 can include avisual identifier to provide an indication of trajectory bifurcationidentified based on the processed VCG signals. For example GUI 900displays both a test loop 902 and a control loop 904 to allow visualcomparison by a caregiver. More particularly, the mean shape of thefirst group control loop trajectory can be displayed as a dashed line ormarked by a visual indicator (e.g., represented by line 902), to enabledifferentiation from the representation of the second “control” group ofloop trajectories (e.g., represented by line 904). In some examples, thedifference between the two loop representations—the control and testgroups—can be shown as a color-coded area on the display that indicatesvisually the differences between the loops. An indicator 906 of the riskscore calculated based at least in part on the differences between thetwo loops can also be provided on display 900.

Referring to FIG. 10, an example method 1000 is shown for determining acardiac risk indicator based on identification of T wave bifurcations.In one embodiment, the method 1000 is implemented by the example patientmonitoring device described herein. However, other embodiments arepossible.

At a step 1002, a patient is monitored, by recording one or more typesof physiological data, including an ECG signal. The ECG signal can bereceived from any appropriate source of patient ECG data. For example,ECG data can be received in real-time from three or more ECG electrodesattached to a patient or previously recorded data can be received from astorage device. ECG data can be of any appropriate type. ECG data can berecorded from a plurality of lead sites on the surface of the patient'sbody. In some implementations, standard 12-lead ECG recordings (e.g.,leads I, II, III, aVR, aVF, aVL, V1, V2, V3, V4, V5 and V6) can bederived based on signals retrieved with 10 ECG electrodes. Anyappropriate number of ECG electrodes, attached to appropriate bodysites, can be used. Examples of other ECG lead systems include the“Frank” electrode lead system (e.g., 6 electrodes), the McFee-ParungaoLead System, the SVEC III Lead System, Fischmann Barber-Weiss LeadSystem, and the Nelson Lead System. Other examples include addition ofright-sided precordial leads, posterior leads, leads placed in higher orlower intercostal spaces, and the like.

In some implementations, information about the source of the ECG datacan be provided to the patient monitoring device 606 (see FIG. 6). Forexample, the patient monitoring device can adapt the configuration ofthe display and/or analysis tools based on the source of the ECG data,such as the position of the ECG leads with respect to the heart, thebody, and/or to other leads. In some implementations, the patientmonitoring device can perform real time ECG signal pre-processing. Realtime ECG signal pre-processing can include removing the DC componentwith a high-pass filter, amplifying the ECG signal, limiting the signalbandwidth with a low-pass filter and digitally sampling the ECG signal.In some implementations, the ECG signal is received together withadditional patient data, including patient statistics, otherphysiological data recordings, medical history, physical exam findingsand other medical information that might be requested by a user. Patientdata can be used in conjunction with patient-specific ECG data for dataprocessing and display, or it can be used to correlate informationextracted from the ECG data.

At step 1004, a VCG signal is determined based on the received ECGsignal. ECG data provides a time-dependent voltage that describes theelectrical activity of the heart, which is treated like a dipole havingan origin at the center of the patient's heart. Multiple ECG lead sitesprovide different time-dependent voltage waveforms that reflect theoverall cardiac electrical activity. A time-dependent heart vector thatrepresents the size and orientation of the time-varying electricaldipole can be calculated by approximating the electrical activity of theheart.

Additionally, at step 1004, three or more ECG leads can be used togenerate the vectorcardiograph, typically using the X, Y, Z orthogonalcomponents for the representation of the VCG vector. A conversion matrixcan be used to convert a particular set of leads to the V_(x), V_(y),V_(z) orthogonal components of the VCG vector. In some implementations,a VCG heart vector can be derived from the ECG using an inversetransform (e.g., an inverse Dower matrix, Levkov matrix). Any conversionmethod can be used to generate the VCG heart vector based on the ECGdata.

At a step 1006, the process determines a loop trajectory including aportion of ECG cycle. The portion of ECG cycle can be a P, QRS or Twave. Determining a portion of the ECG cycle can include detecting anonset of the portion of the ECG cycle and an end of the portion of theECG cycle, isolating the portion of the ECG cycle based on detecting theonset of the portion of the ECG cycle and the end of the portion of theECG cycle and filtering the isolated portion of the ECG cycle. Theportion of ECG cycle can be determined for each cardiac cycle of thereceived ECG.

In some implementations, the information about the plurality of cardiaccycles is used to calculate a characterization of a plurality of cardiaccycles to generate the control loop trajectory; and store the controlloop trajectory. In some implementations, the characterization caninclude a spline estimation of the loop trajectories corresponding to aplurality of cardiac cycles. As mentioned previously, statistical shapeanalysis can be used to characterize the loop or groups of loops. Forexample, a control group of loop trajectories can be generatedautomatically at the beginning of the monitoring session. There can be auser input on the patient monitoring device to allow the user tomanually initiate a new acquisition of the control group of looptrajectories. The control group can be composed of two or more ECGcycles. In some implementations, the control group of loop trajectoriescorresponds to 30 seconds up to 72 hours of ECG data. The time periodcan be configured in the non-volatile storage memory of the patientmonitoring device.

At step 1008, the system characterizes the control group of loops. Ananalysis, such as a statistical one, is performed on the looptrajectories for the different cardiac cycles. As such, the shape of thecontrol loop represents a statistical representation, oftentimes in theform of a mean, of the shapes of observed loop trajectories during thecontrol window.

In another example, the odd beats or even beats are used to generate thecontrol or test loop trajectories. In this way, the analysis can analyzefor the effects of T-wave alternans. Thus, the control group can beevery odd element in a time period, and the test group can be every evenelement in the same time period, or vice versa. This will provide a moreaccurate measure of T-wave alternans. In more elaborate implementations,the shape characteristics of the control and/or test groups can bedetermined using every k^(th) loop, L_(k), of the group under analysis.For instance, for k equal to 3, the control group would be composed ofthe 1^(st), 3^(rd), 6^(th), 9^(th) etc., of the original group underanalysis. The test group can then be composed of the 2^(nd), 5^(th),8^(th), etc. following the end of the control group acquisition period.

In a further elaboration, for any particular value of k, multiplesubgroups of the control period interval can be created, for instance,for k=4, you can start at loop 1, 2, 3 or 4, to create 4 distinct groupsof loops for characterization of the shape using such methods asstatistical shape analysis. A single group from within these subgroupscan be chosen as the control group by analyzing the statistical varianceof the shape of the subgroups and choosing the subgroup that is the mostself-consistent with the lowest variation in shape. Thus, for eachinterval up to about 8 or 10, or whatever is computationally practicallygiven the state of the art with microprocessors, a control group iscreated for each value of k, and at regular intervals, even as rapidlyas every new loop, the test group can also be decomposed into the ksubgroups and compared to the control subgroups for degree of trajectorybifurcation.

In some implementations, the characterization can be based on an averageor median of loop trajectories corresponding to the plurality of cardiaccycles. In some implementations, the test group of loop trajectoriescorresponds ECG data recorded within last 5 seconds up to 60 minutesfrom present time. The time period corresponding to the test group ofloop trajectories can be separated by at least 5 minutes from the timeperiod corresponding to the control group. At steps 1010-1016 the testgroup of loop trajectories is generated. At step 1010 the analysis for anew group of test loops begins. This analysis of a new group of testloops can be based on a time threshold (e.g., a new set of loops isanalyzed every 10 minutes or every 30 minutes) or can be based on aphysiological trigger such as an increased risk score or another factorindicative of a change in the status of the patient. At step 1012, a VCGloop trajectory is determined for a particular beat that will beincluded in the set of test loops. For example, two or more ECG leadscan be used to generate the vectorcardiograph, typically using the X, Y,Z orthogonal components for the representation of the VCG vector. Thesystem determines (e.g., at step 1014) if the length for the test grouphas been reached. For example, the size of the test group can be basedon a threshold number of loops and/or on a time based threshold. If thelength has not been reached, the system continues to determine looptrajectories to add to the test group. If the length has been reached,at step 1016, the system characterizes the test group of loops.

The loop trajectories determined at steps 1006 (control loops) and 1012(test loops) can be at least three dimensional. For example, a firstdimension can include a first spatial component of the loop trajectory,a second dimension can include a second spatial component of the looptrajectory orthogonal to the first dimension and a third dimension caninclude a third spatial component of the loop trajectory orthogonal tothe first dimension and the second dimension. In some implementations,the loop trajectory can include more than three dimensions. For example,a fourth dimension can include an additional physiological signalco-registered with the ECG signal.

At step 1018, the system compares the control group of loops to the testgroup of loops. At step 1020, the trajectory bifurcation is identifiedby comparing the test loop trajectory to a control loop trajectory. Insome implementations, the trajectory bifurcation can be identified basedon a statistical analysis. For example, a variation of the looptrajectory from the control loop trajectory that occurs for a portion ofthe ECG signal and exceeds the standard deviation of the control looptrajectory can be identified as a trajectory bifurcation. As anotherexample, the statistical analysis used for the identification of thetrajectory bifurcation can be a non-Gaussian statistical analysis.

In some implementations, the action of trajectory bifurcationidentification can include calculating an area of the loop trajectoryand subtracting the area of the loop trajectory from an area of thecontrol loop trajectory. In some implementations, the identification ofthe trajectory bifurcation leads to an automatic generation of abifurcation.

In some implementations, the step 1020 is repeated at least three timesto compare the trajectory bifurcation of at least three consecutivecardiac cycles of the plurality of cardiac cycles to determine a trendof the trajectory bifurcation and based on the trend, to define anepisodic trajectory bifurcation. The action of identifying thetrajectory bifurcation can be repeated cyclically over multiple cardiaccycles. For example, the trajectory bifurcation can be identified foreach recorded cardiac cycle, after the control loop trajectory wasdetermined.

At step 1022, an indicator is generated based on the identification ofthe trajectory bifurcation. In some implementations, the indicator caninclude a cardiac risk score (e.g., a quantitative risk estimated value)based on the identification of the trajectory bifurcation and an alarmthat alerts a user of the remote device In some implementations, a userof the remote device can select a treatment plan based on the indicatorthat can be delivered to the monitored patient.

Referring to FIG. 11, an example method 1100 is shown for determining anindicator of a cardiac event. In one embodiment, the method 1100 isimplemented by the example patient monitoring device described herein.The patient monitoring device can be a defibrillator, a pacemaker, acomputing device or another type of device.

At a step 1102, an ECG signal can be received from any appropriatesource of patient ECG data including two or more ECG leads. In someimplementations, the ECG signal is received together with additionalpatient data.

At step 1104, a VCG signal is generated based on the received ECGsignal. Any conversion method can be used to generate the VCG heartvector based on the ECG data. At a step 1106, the process determines aloop trajectory including a portion of ECG signal. The portion of ECGsignal can be a T, QRS or P wave. Determining a portion of the ECGsignal can include detecting an onset of the portion of the ECG signaland an end of the portion of the ECG signal, isolating the portion ofthe ECG signal based on detecting the onset of the portion of the ECGsignal and the end of the portion of the ECG signal and filtering theisolated portion of the ECG signal. The portion of ECG signal can bedetermined for each cardiac cycle of the received ECG.

At step 1108, a trajectory bifurcation is identified by comparingmultiple loop trajectories (e.g., a test loop trajectory to a controlloop trajectory) for a plurality of cardiac cycles. In someimplementations, the trajectory bifurcation can be identified based on astatistical analysis. In some implementations, the step 1108 is repeatedmultiple times to compare the trajectory bifurcation of multipleconsecutive cardiac cycles, to determine a trend of the trajectorybifurcation and, based on the trend, to define an episodic trajectorybifurcation.

At step 1112, an indicator of a cardiac event is determined based on thetrajectory bifurcation. In some implementations, the indicator caninclude a cardiac risk score (e.g., a quantitative risk estimated value)based on the identification of the trajectory bifurcation and an alarmthat alerts a user of the remote device In some implementations, a userof the remote device can select a treatment plan based on the indicatorthat can be delivered to the monitored patient.

Referring to FIG. 12, an example system 1200 for monitoring andprocessing patients VCG is illustrated. For example, a caregiver canview information about multiple patients via a remote device 1202. Thisenables the caregiver to identify patients with increased risk anddeploy the appropriate personnel to attend to the situation anddetermine appropriate treatment. System 1200 includes a remote device1202, connectivity interface(s) 1204, a network 1206, a first facilitysystem 1208, and a second facility system 1210. As discussed in furtherdetail herein, data is transferred from each of the first and secondfacility systems 1208 and 1210 through the network 1206 and connectivityinterface(s) 1204 for presentation, or display on the remote device1202. Further, data can be transferred from the remote device 1202through the connectivity interface(s) 1204 and network 1206 to each ofthe first and second facility systems 1208 and 1210, respectively. Forexample, data associated with a treatment plan for correcting atrajectory bifurcation identified in a VCG of a patient 1209 can betransferred between the remote device 1202 and the correspondingfacility system 1208 or 1210. Although a single remote device 1202 isillustrated, it is contemplated that one or more remote devices 1202 cancommunicate with each of the first and second facility systems 1208,1210 through the network 1206 and connectivity interface(s) 1204.Similarly, although two facility systems are illustrated, the presentdisclosure can be implemented with one or more facility systems.

Implementations of the present disclosure are discussed in furtherdetail herein with reference to an example context. The example contextincludes a patient monitoring processes and in particular identificationof cardiac abnormalities. Within the context example, the cardiacactivity of a patient can be continuously monitored by the patientmonitoring device 606, which includes an ECG recording device. Thepatient monitoring device 606 can include the patient information system1224 and the computer interface 620, forming a part of or a completesystem for indicating cardiac risk.

The system for indicating cardiac risk can provide status changes thatcan occur automatically upon the identification of an event (e.g.,trajectory bifurcation). Executability of the actions and identificationof the events are constrained or guided by strict processing rules,which can vary for different facility systems 1208 and 1210. Forexample, in some cases, patient data is transferred to a remote device1202 at the identification of an event (e.g., trajectory bifurcation).In other cases, data can be transferred upon a request of a user of theremote device 1202.

The remote device 1202 can include any number of devices. Such devicesinclude, but are not limited to, a mobile phone, a smartphone, apersonal digital assistant (PDA), a laptop, a tablet personal computer(PC), a desktop PC, a set-top box, an interactive television and/orcombinations thereof. The remote device 1202 includes a display 1212, aprocessor 1214, memory 1216, an input interface 1218, and acommunication interface 1220.

The remote device 1202 can communicate wirelessly through thecommunication interface(s) 1204, which can include digital signalprocessing circuitry. The communication interface(s) 1204 can providecommunications under various modes or protocols including, but notlimited to, GSM voice calls, SMS, EMS or MMS messaging, CDMA, TDMA, PDC,WCDMA, CDMA2000, and/or GPRS. Such communication can occur, for example,through a radio-frequency transceiver (not shown). Further, the remotedevice can be capable of short-range communication using featuresincluding, but not limited to, Bluetooth and/or WiFi transceivers (notshown).

The remote device 1202 communicates with the network 1206 through theconnectivity interface(s) 1204. The connectivity interface(s) 1204 caninclude, but is not limited to, a satellite receiver, cellular network,a Bluetooth system, a Wi-Fi system (e.g., 1202.x), a cable modem, aDSL/dial-up interface, and/or a private branch exchange (PBX) system.Each of these connectivity interfaces 1204 enables data to betransmitted to/from the network 1206. The network 1206 can be providedas a local area network (LAN), a wide area network (WAN), a wireless LAN(WLAN), a metropolitan area network (MAN), a personal area network(PAN), the Internet, and/or combinations thereof.

In the systems of FIG. 12, the first facility system 1208 includes aplurality of facilities 1222, and the second facility system 1210includes a single facility 1222. Each facility 1208, 1210 or 1222includes an associated patient information system 1224, computerinterface(s) 120, and patient monitoring device(s) 1208. In someimplementations, the patient information system 1224 can include acardiology information system. Although the system architecture 1200includes a patient information system 1224 located at each facility1222, it is contemplated that the facilities 1222 can communicate with acommon patient information system 1224 that is remotely located fromeither facility 1222, or that is located at one of the facilities 1222within the facility system 1208, 1210.

Each patient monitoring device 606 is configured to monitorphysiological characteristics of a particular patient 1209, to generatedata signals based thereon. In the example context of the presentdisclosure, the patient monitoring devices 1208 include ECG recordingdevices and one or more processors. The data signals are communicated tothe patient information system 1224 which can collect patient data basedthereon, and store the data to a patient profile that is associated withthe particular patient. The patient monitoring device 606 cancommunicate with the patient information system 1224 and/or the computerinterface 620 via a direct connection, or remotely through a network(not shown) that can include, but is not limited to, a LAN, a WAN, aWLAN, and/or the Internet.

In some cases, the patient data can include the recorded ECG, dataextracted from the processed ECG (e.g., VCG, portion of ECG, trajectorybifurcation and indicator) and, optionally, additional physiologicaldata coregistered with the ECG. The patient data can be made availablefor display on remote device 1202 and/or directly at the patientmonitoring device 1208. A healthcare provider (e.g., a technician, anurse and/or physician) can augment the patient data by inputtingpatient information that can be stored to a patient information system1224. More specifically, the healthcare provider can input patientinformation corresponding to a particular patient 1209, which patientinformation can be stored to the patient profile.

As discussed above, each patient information system 1224 stores patientdata that can be collected from the patient monitoring devices 1208, aswell as additional patient information, that can include informationthat is input by a healthcare provider. The patient information system1224 communicates the patient data and/or the additional patient data toa data management system (DMS) 1232. The DMS 1232 can be provided as aserver, or a virtual server, that runs server software components, andcan include data storage including, but not limited to, a databaseand/or flat files. In the example system architecture of FIG. 12, acommon DMS 1232 is provided. The DMS 1232 can be common to variousfacility systems 1208, 1210, without being associated with a particularfacility system 1208, 1210. Each patient information system 1224communicates with the DMS 1232 via a direct connection, or remotelythrough a network (not shown) that can include, but is not limited to, aLAN, a WAN, a WLAN, and/or the Internet. The DMS 1232 can communicateancillary information (e.g., treatment plan) to the patient informationsystem 1224. In some implementations, each facility system 1208, 1210can include a corresponding DMS 1232. In such an arrangement, eachpatient information system 1224 communicates patient data, and/oradditional patient data to the DMS 1232.

The example system architecture of FIG. 12, provides for the remotelocation of data collection at the DMS 1232. In such implementations,the DMS 1232 can be provided at a third-party site, remote from any ofthe facilities 1222, or facility systems 1208, 1210.

The DMS 1232 synchronizes and transfers data between the remote device1202, or multiple remote devices 1202, and multiple patient informationsystems 1224. More specifically, the DMS 1232 processes and prepares thepatient data and/or patient information for transfer to and presentationon the remote device 1202 from the patient information system 1224. TheDMS 1232 also processes and prepares ancillary information for transferto and storage in the patient information system 1224 from the remotedevice 1202, or for potential presentation at a corresponding computerinterface 620.

The features described can be implemented in digital electroniccircuitry, or in computer hardware, firmware, software, or incombinations of them. The apparatus can be implemented in a computerprogram product tangibly embodied in an information carrier, e.g., in amachine-readable storage device for execution by a programmableprocessor; and method steps can be performed by a programmable processorexecuting a program of instructions to perform functions of thedescribed implementations by operating on input data and generatingoutput. The described features can be implemented advantageously in oneor more computer programs that are executable on a programmable systemincluding at least one programmable processor coupled to receive dataand instructions from, and to transmit data and instructions to, a datastorage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that can be used,directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program can be written in anyform of programming language, including compiled or interpretedlanguages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment.

Suitable processors for the execution of a program of instructionsinclude, by way of example, both general and special purposemicroprocessors, and the sole processor or one of multiple processors ofany kind of computer. Generally, a processor will receive instructionsand data from a read-only memory or a random access memory or both. Theessential elements of a computer are a processor for executinginstructions and one or more memories for storing instructions and data.Generally, a computer will also include, or be operatively coupled tocommunicate with, one or more mass storage devices for storing datafiles; such devices include magnetic disks, such as internal hard disksand removable disks; magneto-optical disks; and optical disks. Storagedevices suitable for tangibly embodying computer program instructionsand data include all forms of non-volatile memory, including by way ofexample semiconductor memory devices, such as EPROM, EEPROM, and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,ASICs (application-specific integrated circuits).

To provide for interaction with a user, the features can be implementedon a computer having an LCD (liquid crystal display) or LED display fordisplaying information to the user and a keyboard and a pointing devicesuch as a mouse or a trackball by which the user can provide input tothe computer.

The features can be implemented in a computer system that includes aback-end component, such as a data server, or that includes a middlewarecomponent, such as an application server or an Internet server, or thatincludes a front-end component, such as a client computer having agraphical user interface or an Internet browser, or any combination ofthem. The components of the system can be connected by any form ormedium of digital data communication such as a communication network.Examples of communication networks include a local area network (“LAN”),a wide area network (“WAN”), peer-to-peer networks (having ad-hoc orstatic members), grid computing infrastructures, and the Internet.

The computer system can include clients and servers. A client and serverare generally remote from each other and typically interact through anetwork, such as the described one. The relationship of client andserver arises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

Many other implementations other than those described may be employed,and may be encompassed by the following claims.

What is claimed is:
 1. An apparatus comprising a non-volatile computerreadable medium storing instructions for causing a computing system toperform operations comprising: receiving, from two or more ECG leads, anECG signal of a patient; generating at least two orthogonal ECG vectorsbased on the ECG signal; processing the at least two orthogonal ECGvectors to determine a vectorcardiogram loop trajectory of at least aportion of the ECG signal; identifying a trajectory bifurcation bycomparing the vectorcardiogram loop trajectory to a controlvectorcardiogram loop trajectory for at least three consecutive cardiaccycles of a plurality of cardiac cycles; determining a variation of thetrajectory bifurcation over time, the determination based on an analysisof the trajectory bifurcation of the at least three consecutive cardiaccycles; determining an indicator of a cardiac event based on thevariation of the trajectory bifurcation; and delivering a treatment tothe patient based on the determined indicator of the cardiac event. 2.The apparatus of claim 1, wherein the cardiac event comprises animpending acute degeneration of a patient's medical condition.
 3. Theapparatus of claim 2, wherein the impending acute degeneration is one ofa cardiac arrest and a traumatic arrest.
 4. The apparatus of claim 1,wherein the apparatus is a defibrillator.
 5. The apparatus of claim 4,the operations comprising delivering the treatment by the defibrillatorto the patient based on the indicator of the cardiac event.
 6. Theapparatus of claim 1, wherein the two or more ECG leads are includedwithin a wearable defibrillator worn by the patient, and the apparatusis configured to be in communication with the wearable defibrillator. 7.The apparatus of claim 6, wherein the vectorcardiogram loop trajectoryis a test vectorcardiogram loop trajectory that comprises a firstplurality vectorcardiogram loop trajectories obtained during a firsttime period and the control vectorcardiogram loop trajectory comprises asecond plurality of vectorcardiogram loop trajectories obtained during asecond time period that is prior to the first time period.
 8. Theapparatus of claim 7, wherein the first time period comprises a timeperiod in present or recent past and the second time period comprises atime period in a more distant past.
 9. The apparatus of claim 7, whereinthe first time period comprises a time period within 60 minutes of apresent time and the second time period comprises a time period within72 hours of the present time.
 10. The apparatus of claim 7, wherein thefirst time period and the second time period are separated by at least 5minutes.
 11. The apparatus of claim 7, wherein generating the at leasttwo orthogonal ECG vectors based on the ECG signal comprises generatingthe at least two orthogonal ECG vectors during a second period of time,the second period of time occurring subsequent to the first time period.12. The apparatus of claim 6, wherein identifying the trajectorybifurcation further comprises measuring a degree of trajectorybifurcation between the vectorcardiogram loop trajectory and the controlvectorcardiogram loop trajectory.
 13. The apparatus of claim 6, whereinidentifying the trajectory bifurcation further comprises identifyinglandmarks in the vectorcardiogram loop trajectory and in the controlvectorcardiogram loop trajectory.
 14. The apparatus of claim 6, whereinidentifying the trajectory bifurcation further comprises determining ameasure of a shape uncertainty of the vectorcardiogram loop trajectoryand the control vectorcardiogram loop trajectory.
 15. The apparatus ofclaim 14, wherein determining the measure of the shape uncertainty isbased on a statistical analysis of an uncertainty boundary of thevectorcardiogram loop trajectory and the control vectorcardiogram looptrajectory.
 16. The apparatus of claim 6, wherein the cardiac eventcomprises an impending acute degeneration of a patient's medicalcondition.
 17. The apparatus of claim 16, wherein the impending acutedegeneration is one of a cardiac arrest and a traumatic arrest.
 18. Theapparatus of claim 6, wherein the instructions further compriseproviding an alert regarding the cardiac event.
 19. The apparatus ofclaim 6, wherein the instructions further comprise delivering thetreatment to the patient based on the indicator of the cardiac event.20. The apparatus of claim 1, wherein the indicator is determined atdifferent time periods.
 21. The apparatus of claim 20, wherein thedifferent time periods vary between minutes and days.
 22. The apparatusof claim 21, wherein the different time periods comprise at least one of10 minutes, 1 hour, 2 hours, 4 hours, 8 hours, 12 hours, 24 hours, 48hours, and 72 hours.
 23. The apparatus of claim 1, wherein the portionof the ECG signal comprises at least one of a T wave, a P wave and a Swave.
 24. The apparatus of claim 1, the operations comprising displayingthe indicator of the cardiac event.
 25. The apparatus of claim 1,wherein the vectorcardiogram loop trajectory is a test vectorcardiogramloop trajectory that comprises a first plurality vectorcardiogram looptrajectories obtained during a first time period and the controlvectorcardiogram loop trajectory comprises a second plurality ofvectorcardiogram loop trajectories obtained during a second time periodthat is prior to the first time period.
 26. The apparatus of claim 25,wherein the first time period comprises a time period in present orrecent past and the second time period comprises a time period in a moredistant past.
 27. The apparatus of claim 25, wherein the first timeperiod comprises a time period within 60 minutes of a present time andthe second time period comprises a time period within 72 hours of thepresent time.
 28. The apparatus of claim 25, wherein the first timeperiod and the second time period are separated by at least 5 minutes.29. The apparatus of claim 1, wherein identifying the trajectorybifurcation further comprises measuring a degree of the trajectorybifurcation between the vectorcardiogram loop trajectory and the controlvectorcardiogram loop trajectory.
 30. The apparatus of claim 29, whereinmeasuring the degree of the trajectory bifurcation is based on astatistical analysis.
 31. The apparatus of claim 30, wherein thestatistical analysis is a non-Gaussian statistical analysis.
 32. Theapparatus of claim 1, wherein identifying the trajectory bifurcationfurther comprises identifying landmarks in the vectorcardiogram looptrajectory and in the control vectorcardiogram loop trajectory.
 33. Theapparatus of claim 1, wherein identifying the trajectory bifurcationfurther comprises determining a measure of a shape uncertainty of thevectorcardiogram loop trajectory and the control vectorcardiogram looptrajectory.
 34. The apparatus of claim 33, wherein determining themeasure of the shape uncertainty is based on a statistical analysis ofan uncertainty boundary of the vectorcardiogram loop trajectory and thecontrol vectorcardiogram loop trajectory.
 35. The apparatus of claim 1,wherein the computing system comprises a mobile computing device. 36.The apparatus of claim 1, wherein the control vectorcardiogram looptrajectory is generated based on a plurality of predeterminedvectorcardiogram loop trajectories derived from one or more of a P, T,and QRS segments of the ECG signal of the patient during a first periodof time.
 37. A computer-implemented method executed by one or moreprocessors, the method comprising: receiving, from two or more ECGleads, an ECG signal of a patient; generating at least two orthogonalECG vectors based on the ECG signal; processing the at least twoorthogonal ECG vectors to determine a vectorcardiogram loop trajectoryof at least a portion of the ECG signal; identifying a trajectorybifurcation by comparing the vectorcardiogram loop trajectory to acontrol vectorcardiogram loop trajectory for at least three consecutivecardiac cycles of a plurality of cardiac cycles; determining a variationof the trajectory bifurcation over time, the determination based on ananalysis of the trajectory bifurcation of the at least three consecutivecardiac cycles; determining an indicator of a cardiac event based on thevariation of the trajectory bifurcation; and delivering a treatment tothe patient based on the determined indicator of the cardiac event. 38.The computer-implemented method of claim 37, wherein processing the atleast two orthogonal ECG vectors to determine the vectorcardiogram looptrajectory comprises: detecting an onset of the portion of the ECGsignal and an end of the portion of the ECG signal; isolating theportion of the ECG signal based on detecting the onset of the portion ofthe ECG signal and the end of the portion of the ECG signal; andgenerating a vector vectorcardiogram loop based of the portion of theECG signal.
 39. The computer-implemented method of claim 37, whereinidentifying the trajectory bifurcation comprises: calculating an area ofthe vectorcardiogram loop trajectory; and subtracting the area of thevectorcardiogram loop trajectory from an area of the controlvectorcardiogram loop trajectory.
 40. The computer-implemented method ofclaim 39, wherein identifying the trajectory bifurcation furthercomprises: comparing the trajectory bifurcation of the at least threeconsecutive cardiac cycles of the plurality of cardiac cycles;determining a trend of the trajectory bifurcation; and based on thetrend, defining an episodic trajectory bifurcation.
 41. Thecomputer-implemented method of claim 37, further comprising displayingthe indicator of the cardiac event.
 42. The computer-implemented methodof claim 37, wherein the two or more ECG leads are included within awearable defibrillator worn by the patient.